{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOPkbS7Hkr0V/GaIbwyzM8S",
      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/letianzj/QuantResearch/blob/master/workbooks/option_pricer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import copy\n",
        "from enum import Enum\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy.stats import norm\n",
        "from typing import Iterable, List"
      ],
      "metadata": {
        "id": "DEbeNEcUmgN6"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.options.display.float_format = '{:,.5f}'.format"
      ],
      "metadata": {
        "id": "n8Z9hizaoVSQ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/robertmartin8/pValuation"
      ],
      "metadata": {
        "id": "G3lGWNrvo9bN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class OptionType(Enum):\n",
        "  CALL = 1\n",
        "  PUT = 2\n",
        "\n",
        "class EquityOption:\n",
        "\n",
        "  def __init__(\n",
        "      self,\n",
        "      S: float,\n",
        "      K: float,\n",
        "      vol: float = 0.16,\n",
        "      dte: int = 365,\n",
        "      r: float = 0.0,\n",
        "      q: float = 0.0,\n",
        "      typ: OptionType = OptionType.CALL,\n",
        "      mult: float = 100.0,\n",
        "  ) -> None:\n",
        "\n",
        "    self.S = S\n",
        "    self.K = K\n",
        "    self.vol = vol\n",
        "    self.dte = dte\n",
        "    self.r = r\n",
        "    self.q = q\n",
        "    self.typ = typ\n",
        "    self.mult = mult\n",
        "\n",
        "  @property\n",
        "  def T(self) -> float:\n",
        "    return self.dte / 365.0\n",
        "\n",
        "  @property\n",
        "  def d1(self) -> float:\n",
        "    d1 = (np.log(self.S / self.K) + (self.r - self.q + 0.5 * self.vol ** 2) * self.T) / (self.vol * np.sqrt(self.T))\n",
        "    return d1\n",
        "\n",
        "  @property\n",
        "  def d2(self) -> float:\n",
        "    d2 = (np.log(self.S / self.K) + (self.r - self.q - 0.5 * self.vol ** 2) * self.T) / (self.vol * np.sqrt(self.T))\n",
        "    return d2\n",
        "\n",
        "  @property\n",
        "  def price(self) -> float:\n",
        "    result = 0.0\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result = (self.S * np.exp(-self.q * self.T) * norm.cdf(self.d1, 0.0, 1.0) - self.K * np.exp(-self.r * self.T) * norm.cdf(self.d2, 0.0, 1.0))\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result = (self.K * np.exp(-self.r * self.T) * norm.cdf(-self.d2, 0.0, 1.0) - self.S * np.exp(-self.q * self.T) * norm.cdf(-self.d1, 0.0, 1.0))\n",
        "\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def delta(self) -> float:\n",
        "    result = 0.0\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result = np.exp(-self.q * self.T) * norm.cdf(self.d1, 0.0, 1.0)\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result = - np.exp(-self.q * self.T) * norm.cdf(-self.d1, 0.0, 1.0)\n",
        "\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def gamma(self) -> float:\n",
        "    # result = K * np.exp(-r*T) * np.pdf(d2) / S / S / vol / np.sqrt(T)\n",
        "    return np.exp(-self.q*self.T) * norm.pdf(self.d1) / self.S / self.vol / np.sqrt(self.T)\n",
        "\n",
        "  @property\n",
        "  def vega(self) -> float:\n",
        "    # result2 = K * np.exp(-r * T) *  norm.pdf(d2, 0.0, 1.0) * np.sqrt(T)\n",
        "    # assert result == result2, 'vega failed'\n",
        "    return self.S * np.exp(-self.q * self.T) * norm.pdf(self.d1, 0.0, 1.0) * np.sqrt(self.T)\n",
        "\n",
        "  @property\n",
        "  def theta(self) -> float:\n",
        "    result = 0.0\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result = - np.exp(-self.q * self.T) * self.S * norm.pdf(self.d1, 0.0, 1.0) * self.vol / 2 / np.sqrt(self.T) \\\n",
        "                 - self.r * self.K * np.exp(-self.r * self.T) * norm.cdf(self.d2, 0.0, 1.0) \\\n",
        "                 + self.q * self.S * np.exp(-self.q * self.T) * norm.cdf(self.d1, 0.0, 1.0)\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result = - np.exp(-self.q * self.T) * self.S * norm.pdf(-self.d1, 0.0, 1.0) * self.vol / 2 / np.sqrt(self.T) \\\n",
        "                 + self.r * self.K * np.exp(-self.r * self.T) * norm.cdf(-self.d2, 0.0, 1.0) \\\n",
        "                 - self.q * self.S * np.exp(-self.q * self.T) * norm.cdf(-self.d1, 0.0, 1.0)\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def rho(self) -> float:\n",
        "    result = 0.0\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result = self.K * self.T * np.exp(-self.r*self.T) * norm.cdf(self.d2, 0.0, 1.0)\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result = -self.K * self.T * np.exp(-self.r*self.T) * norm.cdf(-self.d2, 0.0, 1.0)\n",
        "\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def vanna(self) -> float:\n",
        "    \"\"\"d^2V/dS/dvol; vega rt spot or delta rt vol\"\"\"\n",
        "    return -np.exp(-self.q*self.T) * norm.pdf(self.d1) * self.d2 / self.vol\n",
        "\n",
        "  @property\n",
        "  def volga(self) -> float:\n",
        "    \"\"\"d^2V/dvol^2, vega rt vol\"\"\"\n",
        "    return self.S*np.exp(-self.q*self.T) * norm.pdf(self.d1) * np.sqrt(self.T) * self.d1 * self.d2 / self.vol\n",
        "\n",
        "  @property\n",
        "  def charm(self) -> float:\n",
        "    \"\"\"delta time decay\"\"\"\n",
        "    result = -np.exp(-self.q * self.T) * norm.pdf(self.d1) * (2 * (self.r - self.q) * self.T - self.d2 * self.vol * np.sqrt(self.T)) / 2 / self.T / self.vol / np.sqrt(self.T)\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result += self.q * np.exp(-self.q * self.T) * norm.cdf(self.d1)\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result -= self.q * np.exp(-self.q * self.T) * norm.cdf(-self.d1)\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def color(self) -> float:\n",
        "    \"\"\"gamma time decay\"\"\"\n",
        "    return -np.exp(-self.q * self.T) * norm.pdf(self.d1) / 2 / self.S / self.T / self.vol / np.sqrt(self.T) * (2 * self.q * self.T + 1 + (2 * (self.r - self.q) * self.T - self.d2 * self.vol * np.sqrt(self.T)) / self.vol / np.sqrt(self.T) * self.d1)\n",
        "\n",
        "  @property\n",
        "  def zomma(self) -> float:\n",
        "    \"\"\"gamma rt vol\"\"\"\n",
        "    return np.exp(-self.q * self.T) * norm.pdf(self.d1) * (self.d1 * self.d2 - 1) / self.S  / self.vol / self.vol / np.sqrt(self.T)\n",
        "\n",
        "  @property\n",
        "  def veta(self) -> float:\n",
        "    \"\"\"vega time decay\"\"\"\n",
        "    return -self.S * np.exp(-self.q * self.T) * norm.pdf(self.d1) * np.sqrt(self.T) * (self.q + (self.r - self.q) * self.d1 / self.vol / np.sqrt(self.T) - (1+self.d1*self.d2)/2/self.T)\n",
        "\n",
        "  def summary(self) -> pd.DataFrame:\n",
        "    df = (\n",
        "          pd.DataFrame.from_dict({\n",
        "          'd1': self.d1,\n",
        "          'd2': self.d2,\n",
        "          'price': self.price,\n",
        "          'delta': self.delta,\n",
        "          'gamma': self.gamma,\n",
        "          'vega': self.vega,\n",
        "          'theta': self.theta,\n",
        "          'rho': self.rho,\n",
        "          'vanna': self.vanna,\n",
        "          'volga': self.volga,\n",
        "          'charm': self.charm,\n",
        "          'color': self.color,\n",
        "          'zomma': self.zomma,\n",
        "          'veta': self.veta,\n",
        "      }, orient='index')\n",
        "      .set_axis(['BS'], axis=1)\n",
        "    )\n",
        "    df['Market'] = df['BS']\n",
        "    df.loc['price', 'Market'] = df.loc['price', 'BS'] * self.mult\n",
        "    df.loc['delta', 'Market'] = df.loc['delta', 'BS'] * self.mult\n",
        "    df.loc['gamma', 'Market'] = df.loc['gamma', 'BS'] * self.mult\n",
        "    df.loc['vega', 'Market'] = df.loc['vega', 'BS'] / 100.0 * self.mult   # Vega is sigma moves 1 or 100%. So 1% is 4.4422; then multiplied by multiplier (cancel out)\n",
        "    df.loc['theta', 'Market'] = df.loc['theta', 'BS'] / 365.0 * self.mult # Theta is 1yr or 365d. So divide by 365 then multiplied by multiplier\n",
        "    df.loc['rho', 'Market'] = df.loc['rho', 'BS'] / 100.0 * self.mult     # Rho is rate moves 1 or 100%, then multiplied by multiplier (cancel out)\n",
        "    # vanna is vega change from vega to vega + vanna when S changes one dollar; or delta changes from delta + vanna/100 when vol changes 1% from vol to vol + 1%\n",
        "    df.loc['vanna', 'Market'] = df.loc['vanna', 'BS'] / 100.0 * self.mult\n",
        "    # volga is vega changes from vega to vega + volga / 100 when vol changes 1% from vol to vol + 1%\n",
        "    df.loc['volga', 'Market'] = df.loc['volga', 'BS'] / 100.0 * self.mult\n",
        "    # charm or delta decay, equals delta changes from delta to delta - charm / 365 when time decays one day\n",
        "    df.loc['charm', 'Market'] = df.loc['charm', 'BS'] / 365.0 * self.mult\n",
        "    # color is gamma time decay, equals gamma changes from gamma to color / 365 when time decays one day ?\n",
        "    df.loc['color', 'Market'] = df.loc['color', 'BS'] / 365.0 * self.mult\n",
        "    # zomma is gamma rt vol, equals gamma changes from gamma to gamma + zomma/100 when vol moves 1% ?\n",
        "    df.loc['zomma', 'Market'] = df.loc['zomma', 'BS'] / 100.0 * self.mult\n",
        "    # veta is vega time decays, equals vega changes from vega to vega / 365 when time decays one day ?\n",
        "    df.loc['veta', 'Market'] = df.loc['veta', 'BS'] / 365.0 / 100.0 * self.mult\n",
        "\n",
        "    df.index.name = 'Call' if self.typ == OptionType.CALL else 'Put'\n",
        "    return df\n",
        "\n",
        "  def implied_vol(self, target_price: float, update_vol: bool=False) -> float:\n",
        "    MAX_ITERATIONS = 500\n",
        "    PRECISION = 1.0e-8\n",
        "    vol_copy = self.vol\n",
        "    self.vol = 0.50\n",
        "    for i in range(MAX_ITERATIONS):\n",
        "      price = self.price\n",
        "      vega = self.vega\n",
        "      diff = target_price - price\n",
        "      if (abs(diff) < PRECISION):\n",
        "        break\n",
        "      self.vol += diff/self.vega   # f(x) / f'(x)\n",
        "    ret = self.vol\n",
        "    if not update_vol:\n",
        "      self.vol = vol_copy\n",
        "    return ret\n",
        "\n",
        "o1 = EquityOption(S=4436, K=4455, r = 0.05, q=0.00, vol=0.156, dte=23, typ=OptionType.CALL)\n",
        "o1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "kk9KlKNhnsbD",
        "outputId": "5ace0209-0173-4333-a979-29f6d677ffdc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               BS      Market\n",
              "Call                         \n",
              "d1       -0.00911    -0.00911\n",
              "d2       -0.04827    -0.04827\n",
              "price    66.87181 6,687.18098\n",
              "delta     0.49637    49.63676\n",
              "gamma     0.00230     0.22965\n",
              "vega    444.22305   444.22305\n",
              "theta  -656.62163  -179.89634\n",
              "rho     134.53519   134.53519\n",
              "vanna     0.12342     0.12342\n",
              "volga     1.25140     1.25140\n",
              "charm    -0.66213    -0.18141\n",
              "color    -0.01819    -0.00498\n",
              "zomma    -0.01471    -0.01471\n",
              "veta  3,531.52666     9.67542"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BS</th>\n",
              "      <th>Market</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Call</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>d1</th>\n",
              "      <td>-0.00911</td>\n",
              "      <td>-0.00911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d2</th>\n",
              "      <td>-0.04827</td>\n",
              "      <td>-0.04827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>66.87181</td>\n",
              "      <td>6,687.18098</td>\n",
              "    </tr>\n",
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              "      <th>delta</th>\n",
              "      <td>0.49637</td>\n",
              "      <td>49.63676</td>\n",
              "    </tr>\n",
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              "      <th>gamma</th>\n",
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              "      <td>0.22965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vega</th>\n",
              "      <td>444.22305</td>\n",
              "      <td>444.22305</td>\n",
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              "      <th>theta</th>\n",
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              "      <th>rho</th>\n",
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              "      <td>134.53519</td>\n",
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              "      <th>vanna</th>\n",
              "      <td>0.12342</td>\n",
              "      <td>0.12342</td>\n",
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              "    <tr>\n",
              "      <th>volga</th>\n",
              "      <td>1.25140</td>\n",
              "      <td>1.25140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charm</th>\n",
              "      <td>-0.66213</td>\n",
              "      <td>-0.18141</td>\n",
              "    </tr>\n",
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              "      <th>color</th>\n",
              "      <td>-0.01819</td>\n",
              "      <td>-0.00498</td>\n",
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              "      <th>zomma</th>\n",
              "      <td>-0.01471</td>\n",
              "      <td>-0.01471</td>\n",
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              "    <tr>\n",
              "      <th>veta</th>\n",
              "      <td>3,531.52666</td>\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0308a80b-597a-403a-9531-c214390f4bfe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(o1.implied_vol(80.0, update_vol=True))\n",
        "print(o1.price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSCkzY3PnrXq",
        "outputId": "32d2ca68-b922-4b0a-d443-c38bb35b9cca"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.18555235412748686\n",
            "80.00000000408772\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class OptionPortfolio:\n",
        "\n",
        "  def __init__(\n",
        "      self,\n",
        "      options: List[EquityOption],\n",
        "      weights: List[float],\n",
        "  ) -> None:\n",
        "\n",
        "    assert len(options) == len(weights), 'options and weights have to match in length'\n",
        "    self.options = copy.deepcopy(options)\n",
        "    self.weights = weights\n",
        "    self._COLUMNS = ['price', 'delta', 'gamma', 'vega', 'theta', 'rho', 'vanna', 'volga', 'charm', 'color', 'zomma', 'veta']\n",
        "\n",
        "  def summary(self) -> pd.DataFrame:\n",
        "    df_bs = (\n",
        "        pd.concat([op.summary()['BS'] * self.weights[idx] for idx, op in enumerate(self.options)], axis=1)\n",
        "        .sum(axis=1)\n",
        "        [self._COLUMNS]\n",
        "    )\n",
        "    df_market = (\n",
        "        pd.concat([op.summary()['Market'] * self.weights[idx] for idx, op in enumerate(self.options)], axis=1)\n",
        "        .sum(axis=1)\n",
        "        [self._COLUMNS]\n",
        "    )\n",
        "    df = (\n",
        "        pd.concat([df_bs, df_market], axis=1)\n",
        "        .set_axis(['BS', 'Market'], axis=1)\n",
        "    )\n",
        "    return df\n",
        "\n",
        "  def span(\n",
        "      self,\n",
        "      x_variable: str,\n",
        "      y_variable: str,\n",
        "      control_variable: str,\n",
        "      x_span: Iterable,\n",
        "      control_span: Iterable,\n",
        "  ) -> None:\n",
        "\n",
        "    self.x_variable = x_variable\n",
        "    self.y_variable = y_variable\n",
        "    self.control_variable = control_variable\n",
        "    self.x_span = x_span\n",
        "    self.control_span = control_span\n",
        "\n",
        "    options_bak = copy.deepcopy(self.options)\n",
        "    control_list = []\n",
        "    for c in control_span:\n",
        "      single_line_dict = dict()\n",
        "      for x in x_span:\n",
        "        for o in self.options:\n",
        "          o.__dict__[self.control_variable] = c\n",
        "          o.__dict__[self.x_variable] = x\n",
        "        single_line_dict[x] = self.summary().loc[y_variable, 'BS']\n",
        "      single_line_ser = pd.Series(single_line_dict)\n",
        "      single_line_ser.name = c\n",
        "      control_list.append(single_line_ser)\n",
        "    df = pd.concat(control_list, axis=1)\n",
        "    self.options = options_bak\n",
        "    return df\n",
        "\n",
        "o1 = EquityOption(S=4300, K=4455, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.CALL)\n",
        "o2 = EquityOption(S=4300, K=4500, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.CALL)\n",
        "\n",
        "op = OptionPortfolio(options=[o1, o2], weights=[1, -1])\n",
        "op.span(\n",
        "    x_variable='S',\n",
        "    y_variable='price',\n",
        "    control_variable='vol',\n",
        "    x_span=np.arange(4000, 4600, 100),\n",
        "    control_span=[0.1, 0.15, 0.2]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "vwpbvMOIsW8n",
        "outputId": "37d99910-0828-4a5d-f8ec-beaf103251a5"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      0.10000  0.15000  0.20000\n",
              "4000  0.00030  0.07817  0.61611\n",
              "4100  0.01665  0.52397  1.94174\n",
              "4200  0.34851  2.31541  4.86683\n",
              "4300  3.06522  7.04875  9.94231\n",
              "4400 12.62500 15.49747 16.97717\n",
              "4500 27.90190 25.94074 24.87951"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-471a756c-2265-4f11-9710-8ba061c31b57\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0.10000</th>\n",
              "      <th>0.15000</th>\n",
              "      <th>0.20000</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4000</th>\n",
              "      <td>0.00030</td>\n",
              "      <td>0.07817</td>\n",
              "      <td>0.61611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4100</th>\n",
              "      <td>0.01665</td>\n",
              "      <td>0.52397</td>\n",
              "      <td>1.94174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4200</th>\n",
              "      <td>0.34851</td>\n",
              "      <td>2.31541</td>\n",
              "      <td>4.86683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4300</th>\n",
              "      <td>3.06522</td>\n",
              "      <td>7.04875</td>\n",
              "      <td>9.94231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4400</th>\n",
              "      <td>12.62500</td>\n",
              "      <td>15.49747</td>\n",
              "      <td>16.97717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4500</th>\n",
              "      <td>27.90190</td>\n",
              "      <td>25.94074</td>\n",
              "      <td>24.87951</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-471a756c-2265-4f11-9710-8ba061c31b57')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-471a756c-2265-4f11-9710-8ba061c31b57 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-471a756c-2265-4f11-9710-8ba061c31b57');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8859feec-9c93-448f-8c33-88ca3f36d6e7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8859feec-9c93-448f-8c33-88ca3f36d6e7')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8859feec-9c93-448f-8c33-88ca3f36d6e7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "o = EquityOption(S=4500, K=4500, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.CALL)\n",
        "op = OptionPortfolio(options=[o], weights=[1])\n",
        "\n",
        "op.span(\n",
        "    x_variable='S',\n",
        "    y_variable='delta',\n",
        "    control_variable='dte',\n",
        "    x_span=np.arange(4000, 5100, 50),\n",
        "    control_span=[1, 30, 60]\n",
        ").plot(figsize=(8, 4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "UheUpCcvI0Nh",
        "outputId": "e565245e-a45a-4a63-8540-607842f6703c"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "o = EquityOption(S=4500, K=4500, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.CALL)\n",
        "op = OptionPortfolio(options=[o], weights=[1])\n",
        "\n",
        "# veta\n",
        "op.span(\n",
        "    x_variable='K',\n",
        "    y_variable='delta',\n",
        "    control_variable='dte',\n",
        "    x_span=np.arange(4000, 5100, 50),\n",
        "    control_span=[1, 30, 60]\n",
        ").plot(figsize=(8, 4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "GpXorePUJ_7p",
        "outputId": "1ee13b23-d90c-4415-dff5-a0dffffccf5b"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 149
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "o1 = EquityOption(S=4500, K=4600, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.CALL)\n",
        "o2 = EquityOption(S=4500, K=4400, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.PUT)\n",
        "rr = OptionPortfolio(options=[o1, o2], weights=[1, -1])\n",
        "\n",
        "fig, ax = plt.subplots(1, 3, figsize=(20, 4))\n",
        "OptionPortfolio(options=[o1], weights=[1]).span(\n",
        "    x_variable='S',\n",
        "    y_variable='vanna',\n",
        "    control_variable='dte',\n",
        "    x_span=np.arange(3000, 5550, 50),\n",
        "    control_span=[1, 30, 60]\n",
        ").plot(ax=ax[0])\n",
        "OptionPortfolio(options=[o2], weights=[1]).span(\n",
        "    x_variable='S',\n",
        "    y_variable='vanna',\n",
        "    control_variable='dte',\n",
        "    x_span=np.arange(3000, 5550, 50),\n",
        "    control_span=[1, 30, 60]\n",
        ").plot(ax=ax[1])\n",
        "rr.span(\n",
        "    x_variable='S',\n",
        "    y_variable='vanna',\n",
        "    control_variable='dte',\n",
        "    x_span=np.arange(3000, 5550, 50),\n",
        "    control_span=[1, 30, 60]\n",
        ").plot(ax=ax[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "LTAKCB7c2tHA",
        "outputId": "05fc016b-1591-480c-afac-6047e421849f"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 166
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "o1 = EquityOption(S=4500, K=4400, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.CALL)\n",
        "o2 = EquityOption(S=4500, K=4500, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.CALL)\n",
        "o3 = EquityOption(S=4500, K=4600, r = 0.05, q=0.00, vol=0.1, dte=23, typ=OptionType.CALL)\n",
        "bf = OptionPortfolio(options=[o1, o2, o3], weights=[1, -2, 1])\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
        "OptionPortfolio(options=[o1], weights=[1]).span(\n",
        "    x_variable='S',\n",
        "    y_variable='volga',\n",
        "    control_variable='dte',\n",
        "    x_span=np.arange(3000, 5550, 50),\n",
        "    control_span=[1, 30, 60]\n",
        ").plot(ax=ax[0])\n",
        "rr.span(\n",
        "    x_variable='S',\n",
        "    y_variable='volga',\n",
        "    control_variable='dte',\n",
        "    x_span=np.arange(3000, 5550, 50),\n",
        "    control_span=[1, 30, 60]\n",
        ").plot(ax=ax[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "S51zcyUEMxzD",
        "outputId": "bb686962-56c2-4546-a26f-4dba5a330eaf"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 170
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class FuturesOption:\n",
        "\n",
        "  def __init__(\n",
        "      self,\n",
        "      F: float,\n",
        "      K: float,\n",
        "      vol: float = 0.16,\n",
        "      dte: int = 365,\n",
        "      r: float = 0.,\n",
        "      typ: OptionType = OptionType.CALL,\n",
        "      mult: float = 100.0,\n",
        "  ) -> None:\n",
        "\n",
        "    self.F = F\n",
        "    self.K = K\n",
        "    self.vol = vol\n",
        "    self.dte = dte\n",
        "    self.r = r\n",
        "    self.typ = typ\n",
        "    self.mult = mult\n",
        "\n",
        "  @property\n",
        "  def T(self) -> float:\n",
        "    return self.dte / 365.0\n",
        "\n",
        "  @property\n",
        "  def d1(self) -> float:\n",
        "    d1 = (np.log(self.F / self.K) + (0.5 * self.vol ** 2) * self.T) / (self.vol * np.sqrt(self.T))\n",
        "    return d1\n",
        "\n",
        "  @property\n",
        "  def d2(self) -> float:\n",
        "    d2 = (np.log(self.F / self.K) - (0.5 * self.vol ** 2) * self.T) / (self.vol * np.sqrt(self.T))\n",
        "    return d2\n",
        "\n",
        "  @property\n",
        "  def price(self) -> float:\n",
        "    result = 0.0\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result = np.exp(-self.r*self.T)*( self.F*norm.cdf(self.d1) - self.K*norm.cdf(self.d2) )\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result = np.exp(-self.r*self.T)*( self.K*norm.cdf(-self.d2) - self.F*norm.cdf(-self.d1) )\n",
        "\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def delta(self) -> float:\n",
        "    result = 0.0\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result = np.exp(-self.r*self.T)*norm.cdf(self.d1)\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result = -np.exp(-self.r*self.T)*norm.cdf(-self.d1)\n",
        "\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def gamma(self) -> float:\n",
        "    # result = k*np.exp(-r*T)*norm.pdf(d2)/F/F/vol/np.sqrt(T)\n",
        "    return np.exp(-self.r*self.T)*norm.pdf(self.d1)/self.F/self.vol/np.sqrt(self.T)\n",
        "\n",
        "  @property\n",
        "  def vega(self) -> float:\n",
        "    # result = K * np.exp(-r*T) * norm.pdf(d2)*np.sqrt(T)\n",
        "    return self.F * np.exp(-self.r * self.T) * norm.pdf(self.d1) * np.sqrt(self.T)\n",
        "\n",
        "  @property\n",
        "  def theta(self) -> float:\n",
        "    result = 0.0\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result = -self.F*np.exp(-self.r*self.T)*norm.pdf(self.d1)*self.vol/2/np.sqrt(self.T) \\\n",
        "                 - self.r*self.K*np.exp(-self.r*self.T)*norm.cdf(self.d2) \\\n",
        "                 + self.r*self.F*np.exp(-self.r*self.T)*norm.cdf(self.d1)\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result = -self.F * np.exp(-self.r * self.T) * norm.pdf(-self.d1) * self.vol / 2 / np.sqrt(self.T) \\\n",
        "                 + self.r * self.K * np.exp(-self.r * self.T) * norm.cdf(-self.d2) \\\n",
        "                 - self.r * self.F * np.exp(-self.r * self.T) * norm.cdf(-self.d1)\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def rho(self) -> float:\n",
        "    result = 0.0\n",
        "    if self.typ == OptionType.CALL:\n",
        "        result = self.K*self.T*np.exp(-self.r*self.T)*norm.cdf(self.d2)\n",
        "    elif self.typ == OptionType.PUT:\n",
        "        result = -self.K*self.T*np.exp(-self.r*self.T)*norm.cdf(-self.d2)\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def vanna(self) -> float:\n",
        "    \"\"\"d^2V/dS/dvol; vega rt spot or delta rt vol\"\"\"\n",
        "    return -np.exp(-self.r*self.T)*norm.pdf(self.d1)*self.d2/self.vol\n",
        "\n",
        "  @property\n",
        "  def volga(self) -> float:\n",
        "    \"\"\"d^2V/dvol^2, vega rt vol\"\"\"\n",
        "    return self.F*np.exp(-self.r*self.T)*norm.pdf(self.d1)*np.sqrt(self.T)*self.d1*self.d2/self.vol\n",
        "\n",
        "o2 = FuturesOption(F=100, K=100)\n",
        "o2.price, o2.delta, o2.gamma, o2.vega, o2.theta, o2.rho, o2.vanna, o2.volga"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zfBNj3rDsmAy",
        "outputId": "e1ed323e-f4d8-4e8e-8f8d-ffe5697b0009"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6.376274402797485,\n",
              " 0.5318813720139874,\n",
              " 0.024854231594475557,\n",
              " 39.76677055116089,\n",
              " -3.1813416440928712,\n",
              " 46.81186279860126,\n",
              " 0.19883385275580445,\n",
              " -1.5906708220464356)"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data"
      ],
      "metadata": {
        "id": "DXKCyHjCpPGN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf"
      ],
      "metadata": {
        "id": "8b12kdmhmkQp"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spy = yf.Ticker(\"SPY\")"
      ],
      "metadata": {
        "id": "YcM5m9f_m3an"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spy.history(period=\"1mo\").head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "hzHKY18qm9FL",
        "outputId": "fe4e22a4-4c87-4db1-db0e-2bb8766643e9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Open        High         Low       Close  \\\n",
              "Date                                                                        \n",
              "2023-07-26 00:00:00-04:00  454.470001  456.989990  453.380005  455.510010   \n",
              "2023-07-27 00:00:00-04:00  459.019989  459.440002  451.549988  452.489990   \n",
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              "2023-07-31 00:00:00-04:00  457.410004  458.160004  456.049988  457.790009   \n",
              "2023-08-01 00:00:00-04:00  456.269989  457.250000  455.489990  456.480011   \n",
              "\n",
              "                             Volume  Dividends  Stock Splits  Capital Gains  \n",
              "Date                                                                         \n",
              "2023-07-26 00:00:00-04:00  71052900        0.0           0.0            0.0  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spy.options"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqvkn8GAnBwL",
        "outputId": "391b2d64-a52e-4a8a-d572-1a9a10585bd4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('2023-08-28',\n",
              " '2023-08-29',\n",
              " '2023-08-30',\n",
              " '2023-08-31',\n",
              " '2023-09-01',\n",
              " '2023-09-05',\n",
              " '2023-09-06',\n",
              " '2023-09-07',\n",
              " '2023-09-08',\n",
              " '2023-09-15',\n",
              " '2023-09-22',\n",
              " '2023-09-29',\n",
              " '2023-10-06',\n",
              " '2023-10-20',\n",
              " '2023-11-17',\n",
              " '2023-12-15',\n",
              " '2023-12-29',\n",
              " '2024-01-19',\n",
              " '2024-03-15',\n",
              " '2024-03-28',\n",
              " '2024-06-21',\n",
              " '2024-06-28',\n",
              " '2024-09-20',\n",
              " '2024-12-20',\n",
              " '2025-01-17',\n",
              " '2025-03-21',\n",
              " '2025-06-20',\n",
              " '2025-12-19')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "calls = spy.option_chain('2023-08-28').calls"
      ],
      "metadata": {
        "id": "dYVO3zTqnIUh"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "calls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "PZ6RQLogoCqN",
        "outputId": "84992ec0-5555-4ec9-f9aa-b62ef42f0cf3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         contractSymbol             lastTradeDate  strike  lastPrice    bid  \\\n",
              "0    SPY230828C00350000 2023-08-23 15:10:49+00:00   350.0      92.06  89.80   \n",
              "1    SPY230828C00360000 2023-08-24 13:42:01+00:00   360.0      83.81  79.81   \n",
              "2    SPY230828C00370000 2023-08-25 16:25:55+00:00   370.0      68.67  69.55   \n",
              "3    SPY230828C00371000 2023-08-24 13:42:01+00:00   371.0      72.82  68.55   \n",
              "4    SPY230828C00372000 2023-08-24 14:20:16+00:00   372.0      71.24  67.83   \n",
              "..                  ...                       ...     ...        ...    ...   \n",
              "107  SPY230828C00478000 2023-08-24 14:01:13+00:00   478.0       0.01   0.00   \n",
              "108  SPY230828C00479000 2023-08-21 14:03:01+00:00   479.0       0.01   0.00   \n",
              "109  SPY230828C00480000 2023-08-24 14:46:01+00:00   480.0       0.01   0.00   \n",
              "110  SPY230828C00505000 2023-08-18 19:50:12+00:00   505.0       0.01   0.00   \n",
              "111  SPY230828C00510000 2023-08-24 13:30:08+00:00   510.0       0.01   0.00   \n",
              "\n",
              "       ask    change  percentChange  volume  openInterest  impliedVolatility  \\\n",
              "0    90.64  0.000000       0.000000    10.0            10           1.183598   \n",
              "1    80.65  0.000000       0.000000     1.0            17           1.062505   \n",
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              "..     ...       ...            ...     ...           ...                ...   \n",
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              "108   0.01  0.000000       0.000000    20.0            20           0.335944   \n",
              "109   0.01  0.000000       0.000000     1.0            49           0.339850   \n",
              "110   0.01  0.000000       0.000000  1988.0          1988           0.515630   \n",
              "111   0.01  0.000000       0.000000     3.0          1097           0.515630   \n",
              "\n",
              "     inTheMoney contractSize currency  \n",
              "0          True      REGULAR      USD  \n",
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              "2          True      REGULAR      USD  \n",
              "3          True      REGULAR      USD  \n",
              "4          True      REGULAR      USD  \n",
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              "108       False      REGULAR      USD  \n",
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              "110       False      REGULAR      USD  \n",
              "111       False      REGULAR      USD  \n",
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              "      <td>True</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SPY230828C00371000</td>\n",
              "      <td>2023-08-24 13:42:01+00:00</td>\n",
              "      <td>371.0</td>\n",
              "      <td>72.82</td>\n",
              "      <td>68.55</td>\n",
              "      <td>69.65</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.835939</td>\n",
              "      <td>True</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SPY230828C00372000</td>\n",
              "      <td>2023-08-24 14:20:16+00:00</td>\n",
              "      <td>372.0</td>\n",
              "      <td>71.24</td>\n",
              "      <td>67.83</td>\n",
              "      <td>68.65</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.916016</td>\n",
              "      <td>True</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>SPY230828C00478000</td>\n",
              "      <td>2023-08-24 14:01:13+00:00</td>\n",
              "      <td>478.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.328132</td>\n",
              "      <td>False</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>SPY230828C00479000</td>\n",
              "      <td>2023-08-21 14:03:01+00:00</td>\n",
              "      <td>479.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.0</td>\n",
              "      <td>20</td>\n",
              "      <td>0.335944</td>\n",
              "      <td>False</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>SPY230828C00480000</td>\n",
              "      <td>2023-08-24 14:46:01+00:00</td>\n",
              "      <td>480.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>49</td>\n",
              "      <td>0.339850</td>\n",
              "      <td>False</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>SPY230828C00505000</td>\n",
              "      <td>2023-08-18 19:50:12+00:00</td>\n",
              "      <td>505.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>1988</td>\n",
              "      <td>0.515630</td>\n",
              "      <td>False</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>SPY230828C00510000</td>\n",
              "      <td>2023-08-24 13:30:08+00:00</td>\n",
              "      <td>510.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1097</td>\n",
              "      <td>0.515630</td>\n",
              "      <td>False</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>USD</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>112 rows × 14 columns</p>\n",
              "</div>\n",
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              "\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.plot(calls['strike'], calls['impliedVolatility'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "bNcfuwcAnfUq",
        "outputId": "978c2e12-e9cb-414b-b212-c9775e911a85"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7acf2a9193c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}